Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments

TL;DR

This paper introduces a trajectory optimization framework for UAVs with fixed cameras to improve maritime target localization in GPS-denied environments, reducing costs and increasing robustness.

Contribution

It presents a novel estimation-aware trajectory optimization method for cooperative UAVs with fixed cameras, outperforming heuristic paths and matching gimballed systems' accuracy.

Findings

Localization error reduced by over 50% with optimized trajectories.

Fixed-camera UAVs achieve accuracy comparable to gimballed systems.

System complexity and cost are substantially lowered.

Abstract

Accurate localization of maritime targets by unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments. UAVs equipped with gimballed electro-optical sensors are typically used to localize targets, however, reliance on these sensors increases mechanical complexity, cost, and susceptibility to single-point failures, limiting scalability and robustness in multi-UAV operations. This work presents a new trajectory optimization framework that enables cooperative target localization using UAVs with fixed, non-gimballed cameras operating in coordination with a surface vessel. This estimation-aware optimization generates dynamically feasible trajectories that explicitly account for mission constraints, platform dynamics, and out-of-frame events. Estimation-aware trajectories outperform heuristic paths by reducing localization error by more than a factor of two, motivating their use in cooperative operations. Results further demonstrate that coordinated UAVs with fixed, non-gimballed cameras achieve localization accuracy that meets or exceeds that of single gimballed systems, while substantially lowering system complexity and cost, enabling scalability, and enhancing mission resilience.